TY - JOUR
T1 - Dense and residual neural networks for full-waveform LiDAR echo decomposition
AU - Liu, Gangping
AU - Ke, Jun
N1 - Publisher Copyright:
©2021TheAuthor(s)
PY - 2021
Y1 - 2021
N2 - For full-waveform LiDAR echo signals, a high efficient and accurate decomposition method based on a dense (Full-waveform Dense Connection Network, FDCN) and a residual neural networks (Full-waveform Deep Residual Network, FDRN) is proposed in this paper.
AB - For full-waveform LiDAR echo signals, a high efficient and accurate decomposition method based on a dense (Full-waveform Dense Connection Network, FDCN) and a residual neural networks (Full-waveform Deep Residual Network, FDRN) is proposed in this paper.
UR - http://www.scopus.com/inward/record.url?scp=85119492187&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:85119492187
SN - 2162-2701
JO - Optics InfoBase Conference Papers
JF - Optics InfoBase Conference Papers
M1 - IF4D.3
T2 - Imaging Systems and Applications, ISA 2021 - Part of OSA Imaging and Applied Optics Congress 2021
Y2 - 19 July 2021 through 23 July 2021
ER -